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142
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ACL
2010
15 years 24 days ago
Experiments in Graph-Based Semi-Supervised Learning Methods for Class-Instance Acquisition
Graph-based semi-supervised learning (SSL) algorithms have been successfully used to extract class-instance pairs from large unstructured and structured text collections. However,...
Partha Pratim Talukdar, Fernando Pereira
105
Voted
ICVS
2003
Springer
15 years 8 months ago
A Spectral Approach to Learning Structural Variations in Graphs
This paper shows how to construct a linear deformable model for graph structure by performing principal components analysis (PCA) on the vectorised adjacency matrix. We commence b...
Bin Luo, Richard C. Wilson, Edwin R. Hancock
134
Voted
EMNLP
2010
15 years 23 days ago
Efficient Graph-Based Semi-Supervised Learning of Structured Tagging Models
We describe a new scalable algorithm for semi-supervised training of conditional random fields (CRF) and its application to partof-speech (POS) tagging. The algorithm uses a simil...
Amarnag Subramanya, Slav Petrov, Fernando Pereira
120
Voted
ICPR
2002
IEEE
16 years 3 months ago
Relational Graph Labelling Using Learning Techniques and Markov Random Fields
This paper introduces an approach for handling complex labelling problems driven by local constraints. The purpose is illustrated by two applications: detection of the road networ...
Denis Rivière, Jean-Francois Mangin, Jean-M...
133
Voted
ICDM
2010
IEEE
226views Data Mining» more  ICDM 2010»
15 years 22 days ago
Edge Weight Regularization over Multiple Graphs for Similarity Learning
The growth of the web has directly influenced the increase in the availability of relational data. One of the key problems in mining such data is computing the similarity between o...
Pradeep Muthukrishnan, Dragomir R. Radev, Qiaozhu ...